In recent years, mobile communications have become a widely used part of everyday life. As user demand for wider varieties of mobile service have expanded, network operators have begun deploying networks that provide wireless wide area communications utilizing packet transport. Such networks support traditional voice and text messaging services, and they support a variety of multimedia data services including services that may require substantially larger throughput than was available through older network technologies. For example, operators of networks based on CDMA (code division multiple access) technologies migrated from IS-95, to cdma200 and are now migrating to single carrier of evolution—data-only (1xEV-DO) network technologies. The 1xEV-DO system is a CDMA based data-only system optimized for high-speed packet data services.
As with any network technology, the network operator must manage the network resources and when appropriate deploy additional or upgraded resources to meet increasing demand for services through the network. For management purposes, a number of techniques have been developed to measure the traffic load on wireless network resources, particularly for earlier network technologies.
One approach is to assign a fixed maximum value (theoretical capacity) of usage to all sector-carriers in a network, based on theoretical calculations. Planning can then be based on the extent to which actual and/or expected usage approaches the theoretical capacity of particular cells of the network. It is well known in the industry that in actual fact, each individual sector of a cell in a system has a different maximum capacity, depending on height above average terrain, clutter environment, geographic distribution of users, etc. Hence, this simplistic approach does not provide an accurate estimate of capacity, in many real-world situations.
Another approach is to estimate the Erlang capacity of each sector carrier by estimating the number of actual users of the sector. The method entails: (1) obtaining operational measurement of transmitted power and peak number of users (note that the quantity “peak number of users” is obtainable from some infrastructure manufacturers); (2) finding the quotient to find “average power per user;” (3) finding the maximum number of permissible users, by dividing the maximum available transmit power by the average power per user; (4) converting the maximum number of users to the equivalent maximum Erlangs of traffic using an Erlang table lookup for a specified grade-of-service; and (5) dividing the maximum Erlangs of traffic by the current Erlangs of traffic to estimate “growth factor.” This method, while more accurate than the first method, has significant disadvantages in application thereof to a wireless network, particular a data-only type wireless network. The Erlang model is predicated on discrete, equal capacity, limited resources (e.g. lines, or trunks in telephony parlance). However, resources of cell sectors in a public wireless network are not used in a discrete and equal manner. In a 1xEV-DO type network, for example, the limited resource is transmission time, users are assigned differing amounts of time based on their link quality and the amount of data they are to receive. Therfore the Erlang models of the past hundred years are not applicable to this field.
Earlier iterations of cellular networks allocated users exactly “enough” power to provide service, but no more, to minimize interference with other coded communications on the band. For such networks, a technique was developed that utilized resource usage data and resource power level data from the wireless network to develop metrics, for example, regarding capacity, usage or performance. However, in the newer technology data-only networks, the power level may be a constant and not a limited resource. For example, in a 1xEV-DO network, the forward channel transmit power for transmitting from the base station to mobile stations is fixed, and users are allocated varying amounts of time for the downstream transmission of their data.
For a data-only network, in which power is fixed, capacity planning has used lagging indicators. Those indicators were the average data speed for the entire sector, or a sample user data speed obtained from drive testing (operating a mobile station through a particular sector, e.g. while driving). The average data speed for the sector is a “noisy” data point and subject to significant variability as more users access the network. Drive testing is time-consuming and expensive, and only yields data valid for the precise time the drive testing was done. Neither method is a leading indicator. Hence, a current deficiency could be detected, but there was no effective way to extrapolate current condition to a potential future deficiency.
Hence a need exists for an accurate technique to estimate values of capacity for resources of a data-only wireless communication network based on actual operations of the network, which allows extrapolation of current operational conditions to predict future performance or capacity needs.